The proposed method is implemented with PyTorch. All experiments are carried out on one GTX1080Ti GPU with 11GB memory. The model is trained using Adam [1]} optimizer. We set the batch to 8. Specifically, we construct a two-stream sampler to simultaneously optimize samples with and without segmentation labels, and the batch size of each part is equally set to 4. The learning rate is set to \(10^{-5}\)  and it linearly decreases to 0 during 40000 iterations. The ResNet50 and Transformer layers are pre-trained with ImageNet [2]}. Empirically, we set \(\lambda _{\text{C}}\) , \(\lambda _{\text{L}}\) , and \(\lambda _{\text{A}}\)  to 0.25, 5.0, and 1.0. For \(\lambda _{\text{D}}\) , we utilize an exponential ramp-up with length 40 for better convergence. We set \(k\)  to 1500. We employ 4 Transformer layers are used to enhance features. The patch size is set to 16 \(\times \)  16.
